diff options
Diffstat (limited to 'src/python/gudhi/sklearn/cubical_persistence.py')
| -rw-r--r-- | src/python/gudhi/sklearn/cubical_persistence.py | 40 |
1 files changed, 19 insertions, 21 deletions
diff --git a/src/python/gudhi/sklearn/cubical_persistence.py b/src/python/gudhi/sklearn/cubical_persistence.py index 329c9435..454cdd07 100644 --- a/src/python/gudhi/sklearn/cubical_persistence.py +++ b/src/python/gudhi/sklearn/cubical_persistence.py @@ -33,8 +33,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin): def __init__( self, dimensions=None, - max_persistence_dimension=0, - only_this_dim=-1, + persistence_dimension=-1, homology_coeff_field=11, min_persistence=0.0, n_jobs=None, @@ -45,20 +44,16 @@ class CubicalPersistence(BaseEstimator, TransformerMixin): Parameters: dimensions (list of int): A list of number of top dimensional cells if cells filtration values will require to be reshaped (cf. :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform`) - max_persistence_dimension (int): The returned persistence diagrams maximal dimension. Default value is `0`. - Ignored if `only_this_dim` is set. - only_this_dim (int): The returned persistence diagrams dimension. If `only_this_dim` is set, - `max_persistence_dimension` will be ignored. - Short circuit the use of :class:`~gudhi.sklearn.post_processing.DimensionSelector` when only one - dimension matters. + persistence_dimension (int or list of int): The returned persistence diagrams dimension(s). + Short circuit the use of :class:`~gudhi.representations.preprocessing.DimensionSelector` when only one + dimension matters (in other words, when `persistence_dimension` is an int). homology_coeff_field (int): The homology coefficient field. Must be a prime number. Default value is 11. min_persistence (float): The minimum persistence value to take into account (strictly greater than - `min_persistence`). Default value is `0.0`. Sets `min_persistence` to `-1.0` to see all values. + `min_persistence`). Default value is `0.0`. Set `min_persistence` to `-1.0` to see all values. n_jobs (int): cf. https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html """ self.dimensions = dimensions - self.max_persistence_dimension = max_persistence_dimension - self.only_this_dim = only_this_dim + self.persistence_dimension = persistence_dimension self.homology_coeff_field = homology_coeff_field self.min_persistence = min_persistence self.n_jobs = n_jobs @@ -75,7 +70,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin): homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence ) return [ - cubical_complex.persistence_intervals_in_dimension(dim) for dim in range(self.max_persistence_dimension + 1) + cubical_complex.persistence_intervals_in_dimension(dim) for dim in self.persistence_dimension ] def __transform_only_this_dim(self, cells): @@ -83,28 +78,31 @@ class CubicalPersistence(BaseEstimator, TransformerMixin): cubical_complex.compute_persistence( homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence ) - return cubical_complex.persistence_intervals_in_dimension(self.only_this_dim) + return cubical_complex.persistence_intervals_in_dimension(self.persistence_dimension) def transform(self, X, Y=None): """ Compute all the cubical complexes and their associated persistence diagrams. Parameters: - X (list of list of double OR list of numpy.ndarray): List of cells filtration values that can be flatten if - `dimensions` is set in the constructor, or already with the correct shape in a numpy.ndarray (and + X (list of list of double OR list of numpy.ndarray): List of cells filtration values that should be flatten + if `dimensions` is set in the constructor, or already with the correct shape in a numpy.ndarray (and `dimensions` must not be set). Returns: + list of pairs or list of list of pairs: Persistence diagrams in the format: - - If `only_this_dim` was set to `n`: `[array( Hn(X[0]) ), array( Hn(X[1]) ), ...]` - - else: `[[array( H0(X[0]) ), array( H1(X[0]) ), ...], [array( H0(X[1]) ), array( H1(X[1]) ), ...], ...]` + - If `persistence_dimension` was set to `n`: `[array( Hn(X[0]) ), array( Hn(X[1]) ), ...]` + - If `persistence_dimension` was set to `[i, j]`: `[[array( Hi(X[0]) ), array( Hj(X[0]) )], [array( Hi(X[1]) ), array( Hj(X[1]) )], ...]` """ - if self.only_this_dim == -1: - # threads is preferred as cubical construction and persistence computation releases the GIL - return Parallel(n_jobs=self.n_jobs, prefer="threads")(delayed(self.__transform)(cells) for cells in X) - else: + # Depends on persistence_dimension is an integer or a list of integer (else case) + if isinstance(self.persistence_dimension, int): # threads is preferred as cubical construction and persistence computation releases the GIL return Parallel(n_jobs=self.n_jobs, prefer="threads")( delayed(self.__transform_only_this_dim)(cells) for cells in X ) + else: + # threads is preferred as cubical construction and persistence computation releases the GIL + return Parallel(n_jobs=self.n_jobs, prefer="threads")(delayed(self.__transform)(cells) for cells in X) + |